Contents

The Flux Capacitor predicts abundances for transcript molecules and alternative splicing events from RNAseq experiments. Additionally, there is a simulation pipeline that is capable to simulate whole transcriptome sequencing experiments.

sgp2

sgp2 is a program to predict genes by comparing anonymous genomic sequences from different species. It combines tblastx, a sequence similarity search program, with geneid, an ab initio gene prediction program.

geneid

geneid is a program to predict genes along a DNA sequence in a large set of organisms. While its accuracy compares favorably to that of other existing tools, geneid is more efficient in terms of speed and memory usage and it offers some rudimentary support to integrate predictions from multiple source.

AcE

AcE is a program to aid gene prediction accuracy evaluation. It uses GFF format to make it easy to convert gene prediction results into an analyzable format. Novel features include isoform accuracy evaluation from either the annotated gene or gene prediction perspective or both at the same time. Masking of genomic sequence which has unknown features allows gene predictions in annotated regions to be analyzed in a genomic context. Test sets, such as an artificial sequence test set or genomic context test set, can be generated by selecting specified annotated sequences from a master set.

A new section has been created: HTML HOWTOs for gff2ps. The first two HOWTOs were also added: "Comparing sources with gff2ps" and "Visualizing PostScript output from gff2ps". We hope you will find them useful.

meta

meta is a program to produce and to align the TF-maps
of two gene promoter regions. meta is very useful to
characterize promoter regions from orthologous genes, or from co-regulated genes
in microarrays, as it reduces the signal/noise
ratio in a very significant manner, still detecting the real functional sites.

mmeta

mmeta is a program to produce and to align the TF-maps of multiple promoter regions. mmeta
is very powerful to characterize promoter regions from multiple orthologous genes, or from
co-regulated genes in microarrays, as it reduces the signal/noise ratio in a very significant
manner, still detecting the real functional sites.